@article{Zhang2022TGRS,
	title={Unsupervised Domain Adaptive {3-D} Detection With Data Adaption From LiDAR Point Cloud},
	author={D. Zhang and X. Wang and Z. Zheng and X. Liu},
	journal={IEEE Transactions on Geosci. Remote Sens.},
	volume={60},
	pages={1-14},
	year={2022},
	publisher={IEEE}
}

@article{VPFNet2023TMM,
	title={{VPFNet}: Improving {3D} object detection with virtual point based {LiDAR} and stereo data fusion},
	author={H. Zhu and J. Deng and Y. Zhang and J. Ji and Q. Mao and H. Li and Y. Zhang},
	journal={IEEE Transactions on Multimedia},
	volume={25},
	pages={5291--5304},
	year={2023},
	publisher={IEEE}
}

@article{VirPNet2024TMM,
	title={{VirPNet}: A Multimodal Virtual Point Generation Network for {3D} Object Detection},
	author={L. Wang and S. Sun and J. Zhao},
	journal={IEEE Transactions on Multimedia},
	volume={26},
	doi={10.1109/TMM.2024.3410117},
	year={2024},
	publisher={IEEE}
}

@article{DA-Net2023TMM,
	title={{DA-Net}: Density-Aware {3D} Object Detection Network for Point Clouds},
	author={S. Wang and K. Lu and J. Xue and Y. Zhao},
	journal={IEEE Transactions on Multimedia},
	volume={25},
	year={2023},
	publisher={IEEE}
}

@article{SP-Det2024TMM,
	title={{SP-Det}: Leveraging Saliency Prediction for Voxel-Based {3D} Object Detection in Sparse Point Cloud},
	author={P. An and Y. Duan and Y. Huang and J. Ma and Y. Chen and L. Wang and Y. Yang and Q. Liu},
	journal={IEEE Transactions on Multimedia},
	volume={26},
	pages={2795--2808},
	year={2024},
	publisher={IEEE}
}

@article{DAS2024TMM,
	title={Domain Adaptive {LiDAR} Point Cloud Segmentation With {3D} Spatial Consistency},
	author={A. Xiao and D. Guan and X. Zhang and S. Lu},
	journal={IEEE Transactions on Multimedia},
	volume={26},
	pages={5536--5547},
	year={2024},
	publisher={IEEE}
}

@article{MSSA2024ToMM,
	title={MSSA: Multi-Representation Semantics-Augmented Set Abstraction for {3D} Object Detection},
	author={H. Liu and J. Du and Y. Zhang and H. Zhang and J. Zeng},
	journal={ACM Transactions on Multimedia Computing, Communications, and Applications},
	volume={25},
	year={2024},
	publisher={ACM}
}

@article{AVP2024TIV,
	title={Adaptation via Proxy: Building Instance-Aware Proxy for Unsupervised Domain Adaptive {3D} Object Detection},
	author={Z. Li and Y. Yao and Z. Quan and L. Qi and Z. Feng and W. Yang},
	journal={IEEE Transactions on Intelligent Vehicles},
	volume={9},
	number={2},
	pages={3478-3492},
	year={2024},
	publisher={IEEE}
}

@inproceedings{ST3D2021CVPR,
	title={{ST3D}: Self-training for Unsupervised Domain Adaptation on {3D} Object Detection},
	author={J. Yang and S. Shi and Z. Wang and H. Li and X. Qi},
	booktitle = {CVPR},
	pages={10368--10378},
	year={2021},
	organization={IEEE}
}

@article{ST3D++2023PAMI,
	title={{ST3D}++: Denoised Self-Training for Unsupervised Domain Adaptation on {3D} Object Detection},
	author={J. Yang and S. Shi and Z. Wang and H. Li and X. Qi},
	journal={IEEE Trans. Pattern Anal. Mach. Intell.},
	volume={45},
	number={5},
	pages={6354-- 6371},
	year={2023},
	publisher={IEEE}
}

@inproceedings{SRDAN2021CVPR,
	title={{SRDAN}: Scale-aware and Range-aware Domain Adaptation Network for Cross-dataset {3D} Object Detection},
	author={W. Zhang and W. Li and D. Xu},
	booktitle={CVPR},
	pages={6769--6779},
	year={2021}
}

@inproceedings{PERE2024CVPR,
	title={Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset {3D} Object Detection},
	author={Z. Zhang and M. Chen and S. Xiao and L. Peng and H. Li and B. Lin and P. Li and W. Wang and B. Wu and D. Cai},
	booktitle={CVPR},
	pages={15291--15300},
	year={2024}
}

@inproceedings{CL3D2023AAAI,
	title={{CL3D}: Unsupervised Domain Adaptation for Cross-{LiDAR 3D} Detection},
	author={X. Peng and X. Zhu and Y. Ma},
	booktitle = {AAAI},
	pages={2047--2055},
	year={2023},
}

@inproceedings{GPA-3D2023ICCV,
	title={{GPA-3D}: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive {3D} Object Detection from Point Clouds},
	author={Z. Li and J. Guo and T. Cao and B Liu and W. Yang},
	booktitle={ICCV},
	pages={6371--6380},
	organization={IEEE},
	year={2023}
}

@inproceedings{REDB2023ICCV,
	title={Revisiting Domain-Adaptive {3D} Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling},
	author={Z. Chen and Y. Luo and Z. Wang and M. Baktashmotlagh and Z. Huang},
	booktitle={ICCV},
	pages={3714--3726},
	organization={IEEE},
	year={2023}
}

@inproceedings{SPG2021ICCV,
	title={{SPG}: Unsupervised Domain Adaptation for {3D} Object Detection via Semantic Point Generation},
	author={Q. Xu and Y. Zhou and W. Wang and C. Qi and D. Anguelov},
	booktitle={ICCV},
	pages={15446--15456},
	organization={IEEE},
	year={2021}
}

@inproceedings{Density2023CVPR,
	title={Density-Insensitive Unsupervised Domain Adaption on {3D} Object Detection},
	author={Q. Hu and D. Liu and W. Hu},
	booktitle={CVPR},
	pages={17556--17566},
	year={2023},
	organization={IEEE}
}

@inproceedings{PointDAN2019NeurIPS,
	title={{PointDAN}: A Multi-Scale {3D} Domain Adaption Network for Point Cloud Representation},
	author={C. Qin and H. You and L. Wang and C. Kuo and Y. Fu},
	booktitle={NeurIPS},
	pages={7190--7201},
	year={2019}
}

@article{zhang2015perceptual,
	title={Perceptual models of preference in 3{D} printing direction},
	author={X. Zhang and X. Le and A. Panotopoulou and E. Whiting and C. Wang},
	journal={ACM Transactions on Graphics (TOG)},
	volume={34},
	number={6},
	pages={1--12},
	year={2015},
	publisher={ACM New York, NY, USA}
}

@inproceedings{ResNetAttention2017,
	author = {F. Wang and M. Jiang and C. Qian and S. Yang and C. Li and H. Zhang and X. Wang and X. Tang},
	title = {Residual attention network for image classification},
	booktitle = {CVPR},
	organization={IEEE},
	pages = {6450--6458}, 
	year = 2017,
}

@inproceedings{GermanyUSA2020CVPR,
	title = {Train in Germany, test in the {USA}: Making {3D} object detectors generalize},
	author = {Y. Wang and X. Chen and Y. You and L. Li and B. Hariharan and M. Campbell and K. Weinberger and W. Chao},
	booktitle = {CVPR},
	organization={IEEE},
	pages = {11710--11723}, 
	year = 2020,
}

@article{SECOND2018MDPI,
	title={{SECOND}: Sparsely Embedded Convolutional Detection},
	author={Y. Yan and Y. Mao and B. Li},
	journal={Sensors},
	volume={18},
	number={10},
	pages={3337.1--3337.17},
	year={2018},
	publisher={MDPI}
}

@article{MMD2006Bioinfo,
	title={Integrating structured biological data by kernel maximum mean discrepancy},
	author={K.Borgwardt and A. Gretton and M. Rasch and H. Kriegel and B Sch\"olkopf and A. Smola},
	journal={Bioinformatics},
	volume={22},
	number={14},
	pages={e49--e57},
	year={2006}
}

@inproceedings{Multi-level2021ICCV,
	title={Unsupervised domain adaptive {3D} detection with multi-level consistency},
	author={Z. Luo and Z. Cai and C. Zhou and G. Zhang and H. Zhao and S. Yi and S. Lu and H. Li and S. Zhang and Z. Liu},
	booktitle={ICCV},
	pages={8846--8855},
	year={2021},
	organization={IEEE}
}

@inproceedings{Attentive2024WACV,
	title={Attentive Prototypes for Source-free Unsupervised Domain Adaptive {3D} Object Detection},
	author={D. Hegde and V. Patel},
	booktitle={WACV},
	pages={3066--3076},
	year={2024},
	organization={IEEE}
}

@inproceedings{PV-RCNN2020CVPR,
	title={{PV-RCNN}: Point-Voxel Feature Set Abstraction for {3D} Object Detection},
	author={S. Shi and C. Guo and L. Jiang and Z. Wang and J. Shi and X. Wang and H. Li},
	booktitle={CVPR},
	pages={10529--10538},
	year={2020},
	organization={IEEE}
}

@inproceedings{VoxelNet2018CVPR,
	title={{VoxelNet}: End-to-End Learning for Point Cloud Based {3D} Object Detection},
	author={Y. Zhou and O. Tuzel},
	booktitle={CVPR},
	pages={4490--4499},
	year={2018},
	organization={IEEE}
}

@inproceedings{PointRCNN2019CVPR,
	title={{PointRCNN}: {3D} Object Proposal Generation and Detection from Point Cloud},
	author={S. Shi and X. Wang and H. Li},
	booktitle={CVPR},
	pages={770--779},
	year={2019},
	organization={IEEE}
}

@article{Transfer2010TNN,
	title={Domain adaptation via transfer component analysis},
	author={S. Pan and I. Tsang and J. Kwok and Q. Yang},
	journal={IEEE Transactions on Neural Networks},
	volume={22},
	number={2},
	pages={199--210},
	year={2011}
}

@inproceedings{FastPoint2019CVPR,
	title={Fast Point {R-CNN}},
	author={Y. Chen and S. Liu and X. Shen and J. Jia},
	booktitle = {ICCV},
	pages={9775--9784},
	year={2019},
	organization={IEEE}
}

@inproceedings{PointNet2017CVPR,
	title={{PointNet}: Deep learning on point sets for 3{D} classification and segmentation},
	author={C. Qi and H. Su and K. Mo and L. Guibas},
	booktitle={CVPR},
	pages={652--660},
	year={2017},
	organization={IEEE}
}

@inproceedings{PointNet++2017NeurIPS,
	title={{PointNet}++: Deep hierarchical feature learning on point sets in a metric space},
	author={C. Qi and L. Yi and H. Su and L. Guibas},
	booktitle={NeurIPS},
	pages={5105--5114},
	year={2017},
}

@inproceedings{VoxelSet2022CVPR,
	title={Voxel Set Transformer: A Set-to-Set Approach to {3D} Object Detection from Point Clouds},
	author={C. He and R. Li and S. Li and L. Zhang},
	booktitle={CVPR},
	pages={8417--8427},
	year={2022},
	organization={IEEE}
}

@inproceedings{Density-Aware2022CVPR,
	title={Point Density-Aware Voxels for {LiDAR 3D} Object Detection},
	author={J. Hu and T. Kuai and S. Waslander},
	booktitle={CVPR},
	pages={8469--8478},
	year={2022},
	organization={IEEE}
}

@inproceedings{KITTI2012CVPR,
	title={Are we ready for autonomous driving? the {KITTI} vision benchmark suite},
	author={Andreas Geiger and Philip Lenz and Raquel Urtasun},
	booktitle={CVPR},
	pages={3354--3361},
	year={2012},
	organization={IEEE}
}

@inproceedings{Waymo2020CVPR,
	title={Scalability in perception for autonomous driving: {Waymo} open dataset.},
	author={P. Sun and H. Kretzschmar and X. Dotiwalla and A. Chouard and V. Patnaik and P. Tsui and J. Guo and Y. Zhou and Y. Chai and B. Caine and et al.},
	booktitle={CVPR},
	pages={2443--2451},
	year={2020},
	organization={IEEE}
}

@inproceedings{MCD2018CVPR,
	title={Maximum classifier discrepancy for unsupervised domain adaptation},
	author={K. Saito and K. Watanabe and Y. Ushiku and T. Harada},
	booktitle={CVPR},
	pages={3723--3732},
	year={2018},
	organization={IEEE}
}

@inproceedings{JDA2013ICCV,
	title={Transfer feature learning with joint distribution adaptation},
	author={M. Long and J. Wang and G. Ding and J. Sun and P. Yu},
	booktitle={ICCV},
	pages={2200--2207},
	year={2013},
	organization={IEEE}
}

@inproceedings{Long2015ICML,
	title={Learning transferable features with deep adaptation networks},
	author={M. Long and Y. Cao and J. Wang and M. Jordan},
	booktitle={ICML},
	pages={97--105},
	year={2015},
	organization={PMLR}
}

@inproceedings{Long2016NeurIPS,
	title={Unsupervised domain adaptation with residual transfer networks},
	author={M. Long and H. Zhu and J. Wang and M. Jordan},
	booktitle={NeurIPS},
	pages={136--144},
	year={2016}
}

@inproceedings{UDA2015ICML,
	title={Unsupervised domain adaptation by backpropagation},
	author={Y. Ganin and V. Lempitsky},
	booktitle={ICML},
	pages={1180--1189},
	year={2015},
	organization={PMLR}
}

@inproceedings{ADDA2017CVPR,
	title={Adversarial discriminative domain adaptation},
	author={E. Tzeng and J. Hoffman and K. Saenko and T. Darrell},
	booktitle={CVPR},
	pages={7167--7176},
	year={2017},
	organization={IEEE}
}

@article{levie2018cayleynets,
	title={Cayleynets: Graph convolutional neural networks with complex rational spectral filters},
	author={R. Levie and F. Monti and X. Bresson and M. Bronstein},
	journal={IEEE Transactions on Signal Processing},
	volume={67},
	number={1},
	pages={97--109},
	year={2018},
	publisher={IEEE}
}

% --graph pt completion related
@article{bronstein2017geometric,
	title={Geometric deep learning: going beyond euclidean data},
	author={M. Bronstein and J. Bruna and Y. LeCun and A. Szlam and P. Vandergheynst},
	journal={IEEE Signal Processing Magazine},
	volume={34},
	number={4},
	pages={18--42},
	year={2017},
	publisher={IEEE}
}

@inproceedings{Semantic2019ICCV,
	title={Semantic-transferable weakly-supervised endoscopic lesions segmentation},
	author={J. Dong and Y. Cong and G. Sun and D. Hou},
	booktitle={ICCV},
	pages={10712--10721},
	year={2019},
	organization={IEEE}
}

% DGCNN
@article{wang2019dynamic,
	title={Dynamic graph cnn for learning on point clouds},
	author={Y. Wang and Y. Sun and Z. Liu and S. Sarma and M. Bronstein and J. Solomon},
	journal={ACM Transactions On Graphics},
	volume={38},
	number={5},
	pages={1--12},
	year={2019},
	publisher={ACM New York, NY, USA}
}

% LDGCNN
@article{zhang2019linked,
	title={Linked dynamic graph cnn: Learning on point cloud via linking hierarchical features},
	author={K. Zhang and M. Hao and J. Wang and C. de Silva and C. Fu},
	journal={arXiv preprint arXiv:1904.10014},
	year={2019}
}

@article{kipf2016semi,
	title={Semi-supervised classification with graph convolutional networks},
	author={T. Kipf and M. Welling},
	journal={arXiv preprint arXiv:1609.02907},
	year={2016}
}

@inproceedings{FastRCNN2018CVPR,
	title={Domain adaptive faster {R-CNN} for object detection in the wild},
	author={Y. Chen and Wen Li and C. Sakaridis and D. Dai and L. Gool},
	booktitle={CVPR},
	pages={3339--3348},
	year={2018},
	organization={IEEE}
}